Inefficient growth of OpenBitSet

Details

Description

Hi, I found a potentially serious efficiency problem with OpenBitSet.

One typical (I think) way to build a bit set is to set() the bits one by one -
e.g., have a HitCollector set() the bit for each matching document.
The underlying array of longs needs to grow as more as more bits are set, of
course.

But looking at the code, it appears to me that the array grows very
ineefficiently - in the worst case (when doc ids are sorted, as they would
normally be in the HitCollector case for example), copying the array again
and again for every added bit... The relevant code in OpenBitSet.java is:

As you can see, if the bits array is not long enough, a new one is
allocated at exactly the right size - and in the worst case it can grow
just one word every time...

Shouldn't the growth be more exponential in nature, e.g., grow to the maximum
of index+1 and twice the existing size?

Alternatively, if the growth is so inefficient, this should be documented,
and it should be recommended to use the variant of the constructor with the
correct initial size (e.g., in the HitCollector case, the number of documents
in the index). and the fastSet() method instead of set().

Activity

I don't know about doubling the array size every time. There's ArrayUtil.getNextSize (a Lucene class) which seems to grow arrays in a mild fashion. the method is well documented, and I think it should be used by ensureCapacityWords.

Shai Erera
added a comment - 08/Sep/09 13:41 I don't know about doubling the array size every time. There's ArrayUtil.getNextSize (a Lucene class) which seems to grow arrays in a mild fashion. the method is well documented, and I think it should be used by ensureCapacityWords.

Michael McCandless
added a comment - 08/Sep/09 14:13 There's ArrayUtil.getNextSize (a Lucene class) which seems to grow arrays in a mild fashion. the method is well documented, and I think it should be used by ensureCapacityWords.
+1

Hi Shai, I guess you're right that if there's such a utility function, we should probably use it.
I just wonder what is the rationale behind the specific formula in this function - basically newsize = oldsize * 1.125 + 6. This formula ensures that at worst case, just 6% of the array space is wasted (instead of 50% in the doubling approach), but the number of reallocations and copies is 8 times higher, and performance is proportionally slower (although obviously, both are linear in amortized time - which the current code isn't). Was there any thought given to why the factor 0.125 is better than 0.25, 0.5, 0.01 or 1.0? I'm not saying that 1.0 (doubling) is best, just that I don't know why 0.125 is.

Nadav Har'El
added a comment - 09/Sep/09 06:28 Hi Shai, I guess you're right that if there's such a utility function, we should probably use it.
I just wonder what is the rationale behind the specific formula in this function - basically newsize = oldsize * 1.125 + 6. This formula ensures that at worst case, just 6% of the array space is wasted (instead of 50% in the doubling approach), but the number of reallocations and copies is 8 times higher, and performance is proportionally slower (although obviously, both are linear in amortized time - which the current code isn't). Was there any thought given to why the factor 0.125 is better than 0.25, 0.5, 0.01 or 1.0? I'm not saying that 1.0 (doubling) is best, just that I don't know why 0.125 is.

This formula ensures that at worst case, just 6% of the array space is wasted

You mean 12.5% right?

I just wonder what is the rationale behind the specific formula in this function

It's just a standard time/space tradeoff, that favors not wasting too much space. This code was "borrowed" from Python's "listobject.c" sources, ie, it governs how Python over-allocates the storage for its list type.

We could explore different constants though I'd be nervous about making this value much higher. Often the consumer of this API will see rapid growth initially, and then the collection stops growing and is re-used for a long time, in which case the long-term wasted RAM is (I think) more important than the one-time short-term CPU cost of finding the "natural" size.

Michael McCandless
added a comment - 09/Sep/09 09:20 This formula ensures that at worst case, just 6% of the array space is wasted
You mean 12.5% right?
I just wonder what is the rationale behind the specific formula in this function
It's just a standard time/space tradeoff, that favors not wasting too much space. This code was "borrowed" from Python's "listobject.c" sources, ie, it governs how Python over-allocates the storage for its list type.
We could explore different constants though I'd be nervous about making this value much higher. Often the consumer of this API will see rapid growth initially, and then the collection stops growing and is re-used for a long time, in which case the long-term wasted RAM is (I think) more important than the one-time short-term CPU cost of finding the "natural" size.

I wanted to add just one comment:
For nomal Lucene usage, the auto-grow of the array is not needed. All internal Lucene code (collectors, filters) use IndexReader.maxDoc() as initial size param to the ctor. If you write own HitCollectors/Collectors(2.9), use the maxDoc() of the current IndexReader. With the new 2.9 Collectors, you would initialize the OpenBitSet in Collector.setNextReader().

Uwe Schindler
added a comment - 09/Sep/09 11:58 I wanted to add just one comment:
For nomal Lucene usage, the auto-grow of the array is not needed. All internal Lucene code (collectors, filters) use IndexReader.maxDoc() as initial size param to the ctor. If you write own HitCollectors/Collectors(2.9), use the maxDoc() of the current IndexReader. With the new 2.9 Collectors, you would initialize the OpenBitSet in Collector.setNextReader().